Information generation method and apparatus

Abstract

Embodiments of the invention disclose an information generation method and apparatus. A specific embodiment of the method comprises the steps of obtaining to-be-analyzed information according to a target keyword; inputting the to-be-analyzed information to a pre-built emotional analysis model to generate an emotional tendency information of the to-be-analyzed information, wherein the emotional analysis model is obtained through the following training steps of obtaining unlabeled sample data and labeled sample data; generating label information corresponding to the unlabeled sample data by using a pre-built label generation model, wherein the unlabeled sample data and the generated label information are taken as expanded sample data, and the label generation model is used for representinga corresponding relationship between the unlabeled sample data and the label information; and performing training by using the labeled sample data and the expanded sample data to obtain the emotionalanalysis model. According to the method and the apparatus, the expanded sample data is automatically generated; sample set data is expanded; the manual labeling cost is reduced; and the accuracy of the information generated by the emotional analysis model is improved.

Description

technical field [0001] The embodiments of the present application relate to the field of computer technology, specifically to the field of Internet technology, and in particular to a method and device for generating information. Background technique [0002] Text sentiment analysis (also known as opinion mining) refers to the use of natural language processing, text mining, and computer linguistics to identify and extract subjective information from raw materials. In some scenarios (such as poll monitoring, public opinion monitoring, trend warning, etc.), it is necessary to monitor the emotional tendency of data obtained based on target keywords (such as Weibo data, news data, post bar data, etc.), mainly divided into positive, neutral and negative. At this stage, a sentiment analysis model can be established based on a supervised learning method. According to the supervised learning method, a large amount of data needs to be labeled, so that the distribution of emotional w...

AI Technical Summary

Problems solved by technology

At this stage, a sentiment analysis model can be established based on a supervised learning method. According to the supervised learning method, a large amount of data needs to be labeled, so that the distribution of emotional words and sentence patterns is as wide as possible, so that the training effect is better, but for Labeling a large amount of data requires a lot of manpower and material resources

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